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Predicting Antitumor Activity of Peptides by Consensus of Regression Models Trained on a Small Data Sample
Predicting antitumor activity of compounds using regression models trained on a small number of compounds with measured biological activity is an ill-posed inverse problem. Yet, it occurs very often within the academic community. To counteract, up to some extent, overfitting problems caused by a sma...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Molecular Diversity Preservation International (MDPI)
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3257078/ https://www.ncbi.nlm.nih.gov/pubmed/22272081 http://dx.doi.org/10.3390/ijms12128415 |
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author | Radman, Andreja Gredičak, Matija Kopriva, Ivica Jerić, Ivanka |
author_facet | Radman, Andreja Gredičak, Matija Kopriva, Ivica Jerić, Ivanka |
author_sort | Radman, Andreja |
collection | PubMed |
description | Predicting antitumor activity of compounds using regression models trained on a small number of compounds with measured biological activity is an ill-posed inverse problem. Yet, it occurs very often within the academic community. To counteract, up to some extent, overfitting problems caused by a small training data, we propose to use consensus of six regression models for prediction of biological activity of virtual library of compounds. The QSAR descriptors of 22 compounds related to the opioid growth factor (OGF, Tyr-Gly-Gly-Phe-Met) with known antitumor activity were used to train regression models: the feed-forward artificial neural network, the k-nearest neighbor, sparseness constrained linear regression, the linear and nonlinear (with polynomial and Gaussian kernel) support vector machine. Regression models were applied on a virtual library of 429 compounds that resulted in six lists with candidate compounds ranked by predicted antitumor activity. The highly ranked candidate compounds were synthesized, characterized and tested for an antiproliferative activity. Some of prepared peptides showed more pronounced activity compared with the native OGF; however, they were less active than highly ranked compounds selected previously by the radial basis function support vector machine (RBF SVM) regression model. The ill-posedness of the related inverse problem causes unstable behavior of trained regression models on test data. These results point to high complexity of prediction based on the regression models trained on a small data sample. |
format | Online Article Text |
id | pubmed-3257078 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Molecular Diversity Preservation International (MDPI) |
record_format | MEDLINE/PubMed |
spelling | pubmed-32570782012-01-23 Predicting Antitumor Activity of Peptides by Consensus of Regression Models Trained on a Small Data Sample Radman, Andreja Gredičak, Matija Kopriva, Ivica Jerić, Ivanka Int J Mol Sci Article Predicting antitumor activity of compounds using regression models trained on a small number of compounds with measured biological activity is an ill-posed inverse problem. Yet, it occurs very often within the academic community. To counteract, up to some extent, overfitting problems caused by a small training data, we propose to use consensus of six regression models for prediction of biological activity of virtual library of compounds. The QSAR descriptors of 22 compounds related to the opioid growth factor (OGF, Tyr-Gly-Gly-Phe-Met) with known antitumor activity were used to train regression models: the feed-forward artificial neural network, the k-nearest neighbor, sparseness constrained linear regression, the linear and nonlinear (with polynomial and Gaussian kernel) support vector machine. Regression models were applied on a virtual library of 429 compounds that resulted in six lists with candidate compounds ranked by predicted antitumor activity. The highly ranked candidate compounds were synthesized, characterized and tested for an antiproliferative activity. Some of prepared peptides showed more pronounced activity compared with the native OGF; however, they were less active than highly ranked compounds selected previously by the radial basis function support vector machine (RBF SVM) regression model. The ill-posedness of the related inverse problem causes unstable behavior of trained regression models on test data. These results point to high complexity of prediction based on the regression models trained on a small data sample. Molecular Diversity Preservation International (MDPI) 2011-11-29 /pmc/articles/PMC3257078/ /pubmed/22272081 http://dx.doi.org/10.3390/ijms12128415 Text en © 2011 by the authors; licensee MDPI, Basel, Switzerland. http://creativecommons.org/licenses/by/3.0 This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Article Radman, Andreja Gredičak, Matija Kopriva, Ivica Jerić, Ivanka Predicting Antitumor Activity of Peptides by Consensus of Regression Models Trained on a Small Data Sample |
title | Predicting Antitumor Activity of Peptides by Consensus of Regression Models Trained on a Small Data Sample |
title_full | Predicting Antitumor Activity of Peptides by Consensus of Regression Models Trained on a Small Data Sample |
title_fullStr | Predicting Antitumor Activity of Peptides by Consensus of Regression Models Trained on a Small Data Sample |
title_full_unstemmed | Predicting Antitumor Activity of Peptides by Consensus of Regression Models Trained on a Small Data Sample |
title_short | Predicting Antitumor Activity of Peptides by Consensus of Regression Models Trained on a Small Data Sample |
title_sort | predicting antitumor activity of peptides by consensus of regression models trained on a small data sample |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3257078/ https://www.ncbi.nlm.nih.gov/pubmed/22272081 http://dx.doi.org/10.3390/ijms12128415 |
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